A World Model Learned to Tell Actions Apart

Most world models minimize error on the transition that actually happened, even though model-predictive control must compare several actions from the same state. AD-WM adds action-recovery objectives during training, then discards their auxiliary heads at test time. Against a matched latent-world-model baseline, the authors report gains in four of five simulated environments and zero-shot transfer to a Franka setup without lab-specific adaptation. The results belong to the tested tasks and encoders; they support action discrimination as a planning objective rather than a general robotics guarantee.